Automated Feature-Specific Tree Species Identification from Natural Images using Deep Semi-Supervised Learning

TL;DR

This paper introduces a deep semi-supervised learning approach for accurate tree species identification from natural images, leveraging minimal labeled data and achieving high accuracy in real-world conditions.

Contribution

It presents a novel semi-supervised method for feature-specific tree species identification that outperforms traditional supervised techniques with limited labeled data.

Findings

Achieved 93.96% accuracy for leaf identification

Achieved 93.11% accuracy for bark identification

Semi-supervised method attained 94.04% top-5 accuracy for leaves

Abstract

Prior work on plant species classification predominantly focuses on building models from isolated plant attributes. Hence, there is a need for tools that can assist in species identification in the natural world. We present a novel and robust two-fold approach capable of identifying trees in a real-world natural setting. Further, we leverage unlabelled data through deep semi-supervised learning and demonstrate superior performance to supervised learning. Our single-GPU implementation for feature recognition uses minimal annotated data and achieves accuracies of 93.96% and 93.11% for leaves and bark, respectively. Further, we extract feature-specific datasets of 50 species by employing this technique. Finally, our semi-supervised species classification method attains 94.04% top-5 accuracy for leaves and 83.04% top-5 accuracy for bark.